boolishs/xstest-v2-copy

---
license: cc-by-4.0
dataset_info:
features:
- name: id
dtype: string
- name: type
dtype: string
- name: prompt
dtype: string
- name: completion
dtype: string
- name: annotation_1
dtype: string
- name: annotation_2
dtype: string
- name: agreement
dtype: bool
- name: final_label
dtype: string
splits:
- name: gpt4
num_bytes: 264757
num_examples: 450
- name: llama2new
num_bytes: 523074
num_examples: 450
- name: llama2orig
num_bytes: 476580
num_examples: 450
- name: mistralguard
num_bytes: 309478
num_examples: 450
- name: mistralinstruct
num_bytes: 377642
num_examples: 450
- name: prompts
num_bytes: 47890
num_examples: 450
download_size: 930253
dataset_size: 1999421
configs:
- config_name: default
data_files:
- split: gpt4
path: data/gpt4-*
- split: llama2new
path: data/llama2new-*
- split: llama2orig
path: data/llama2orig-*
- split: mistralguard
path: data/mistralguard-*
- split: mistralinstruct
path: data/mistralinstruct-*
- split: prompts
path: data/prompts-*
task_categories:
- conversational
language:
- en
size_categories:
- 1K<n<10K
---
# XSTest Dataset for Testing Exaggerated Safety

Note, this is an upload of the data found [here](https://github.com/paul-rottger/exaggerated-safety) for easier research use.
All credit to the authors of the [paper](https://arxiv.org/abs/2308.01263)

The test prompts are subject to Creative Commons Attribution 4.0 International license. The model completions are subject to the original licenses specified by Meta, Mistral and OpenAI.

### Loading the dataset
Use the following:
```
from datasets import load_dataset
dataset = load_dataset("natolambert/xstest-v2-copy)
prompts = dataset["prompts"] # select the correct subset(s)
```

The dataset is a `DatasetDict` with the following structure:
```
DatasetDict({
gpt4: Dataset({
features: ['id', 'type', 'prompt', 'completion', 'annotation_1', 'annotation_2', 'agreement', 'final_label'],
num_rows: 450
})
llama2new: Dataset({
features: ['id', 'type', 'prompt', 'completion', 'annotation_1', 'annotation_2', 'agreement', 'final_label'],
num_rows: 450
})
llama2orig: Dataset({
features: ['id', 'type', 'prompt', 'completion', 'annotation_1', 'annotation_2', 'agreement', 'final_label'],
num_rows: 450
})
mistralguard: Dataset({
features: ['id', 'type', 'prompt', 'completion', 'annotation_1', 'annotation_2', 'agreement', 'final_label'],
num_rows: 450
})
mistralinstruct: Dataset({
features: ['id', 'type', 'prompt', 'completion', 'annotation_1', 'annotation_2', 'agreement', 'final_label'],
num_rows: 450
})

})
```

The mapping for prompts is as follows (from the original below)
* `id_v1` removed,
* `id_v2` -> `id`
* empty column completion
* focus to annotation_1 and note to annotation_2
* empty `final_label`
```
prompts: Dataset({
features: ['id_v1', 'id_v2', 'type', 'prompt', 'focus', 'note'],
num_rows: 450
})
```
Please cite their work:
```
@misc{röttger2023xstest,
title={XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models},
author={Paul Röttger and Hannah Rose Kirk and Bertie Vidgen and Giuseppe Attanasio and Federico Bianchi and Dirk Hovy},
year={2023},
eprint={2308.01263},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```